Due to the expansion of small-scale distributed generation, residential consumers are evolving to active participants in energy markets. Concepts like Local Energy Markets (LEM) are designed to harvest flexibility of these prosumers and contribute to a stable power system operation. However, the stochastic nature of the consumption of households increases the difficulty of accurate forecasts and can lead to erroneous bids and penalty payments. State of the art load forecasting methods can reduce this error to a certain extend. Yet, for a systematic assessment of the implications of forecast errors, a method capable of generating forecast time series with defined errors is required. With this method, measures to decrease the implications of forecast errors (e.g., aggregation of participants) can be evaluated. In this paper, we introduce such a method based on nonlinear optimization. After an analysis of typically used error metrics and achieved forecast errors in the literature, the proposed method is evaluated using German household load profiles demonstrating similar statistical properties as found in the literature. Additionally, we show the application of the method to a LEM simulation case revealing that a participation of a household without flexible assets would only be profitable for forecast errors instead of accuracies below 30-40%.